Detection algorithm dataset for PWD in complex environments

The drone was flown at relative altitudes ranging from 50 to 120 meters above ground level, following an "S"-shaped flight path to comprehensively cover areas populated with pine trees.Based on the progression of pine wilt disease symptoms, the collected data were categorized into two classes: "early stage" and "late stage." A total of 2,775 images were compiled and divided into training, validation, and test sets in an 8:1:1 ratio.

To expand the dataset, a series of augmentation techniques—including flipping, scaling, noise addition, and cropping—were applied, increasing the total number of images to 18,375.Addressing the challenge of reduced detection performance in complex environments, an improved YOLOv5 model was developed to enhance identification accuracy and support large-scale, rapid monitoring. All data and code used in the analysis have been made publicly available through Mendeley Data to ensure experimental reproducibility.